Abstract

The differentiation between obstructive and
central respiratory events is one of the most recurrent tasks in the diagnosis of sleep disordered breathing. Esophageal
pressure measurement is the gold-standard method to assess respiratory effort and identify these events. But as its
invasiveness discourages its use in clinical routine, noninvasisve systems have been proposed for differentiation. However, their adoption has been slow due to their limited
clinical validation, as the creation of manual, gold-standard validation sets by human experts is a cumbersome procedure.
In this study, a new system is proposed for an objective automatic, gold-standard differentiation between obstructive
and central hypopneas with the esophageal pressure signal.
First, an overall of 356 hypopneas of 16 patients were manually scored by a human expert to create a gold-standard validation
set. Then, features were extracted from each hypopnea to train and test classifiers (Discriminant Analysis, Support Vector
Machines and adaboost classifiers) to differentiate between central and obstructive hypopneas with the gold-standard
esophageal pressure signal. The automatic differentiation system achieved promising results, with a sensitivity of 0.88, a
specificity of 0.93 and an accuracy of 0.90. Hence, this system seems promising for an automatic, gold-standard differentiation between obstructive and central hypopneas.